Your organisation accrues a lot of data every day, but what are you doing to maximise its value?
Artificial Intelligence (AI) is becoming a popular method of processing, interpreting, and deriving insights from large datasets. In Australia, 51% of companies plan to devote 10-20% of their IT budgets to AI. Spending will increase in further months, with 29% expecting their AI spending to rise by up to half and a further 9% predicting an increase of over 50%.
Rather than letting data lay dormant, using AI for data analysis enables your business to speed up insight generation, improve accuracy, enable predictive analytics, and reduce manual tasks. However, while AI-driven data analysis offers significant benefits, it also comes with key challenges. The blog outlines the opportunities and challenges of using AI for data analysis.
Source: KPMG.
The opportunities in using AI for data analysis
1. Get key insights faster
AI in data analysis enables your business to process large datasets and access critical insights much faster than traditional methods. AI algorithms can analyse vast amounts of information to identify trends or patterns that human analysts may overlook.
Moreover, the speed at which AI processes data can help your business adapt by staying ahead of the competition and consumer behaviour. As a result, your organisation can respond to emerging trends and potential issues to maintain a competitive edge.
2. Improving data accuracy
AI enhances data analysis accuracy by minimising human error and providing consistent, precise results. Machine learning models, in particular, can learn from data over time, improving their accuracy as they adapt to new information. As such, they become more capable of producing reliable and accurate analyses. In addition to improving accuracy, AI helps identify and correct data inconsistencies, leading to cleaner datasets.
3. Predictive capabilities
By analysing historical data, AI can forecast future trends and behaviours, providing businesses with valuable foresight. This predictive power allows companies to anticipate market shifts, manage risks effectively, and identify new growth opportunities, making AI an indispensable tool for strategic planning.
Additionally, predictive analytics can help businesses optimise their operations by identifying potential problems before they occur. This proactive approach enables companies to implement preventive measures, reducing downtime and improving operational efficiency.
4. Reduced manual work
AI automates time-consuming and repetitive tasks associated with data analysis, reducing the need for manual labour. Automation speeds up the analysis process and allows human analysts to complete complex, strategic tasks that require critical thinking and creativity.
What challenges arise when using AI for data analysis?
1. Data preprocessing and quality
The effectiveness of AI-driven data analysis depends on the quality of the input data. Poor data quality means inaccurate results and misleading insights. Ensuring high-quality data is particularly challenging, given the sheer volume, velocity, and variety of data organisations deal with today. Datasets often come from disparate sources, leading to inconsistency, incompleteness, and inaccuracies. Therefore, data preprocessing, including cleaning, transforming, and integrating data, is a critical step that requires significant time and resources to ensure the success of AI models.
2. Getting the right talent
The interdisciplinary nature of AI requires a blend of expertise in machine learning, data science and domain-specific knowledge, making it challenging for organisations to find qualified personnel. Your organisation may struggle to realise AI’s full potential without the right talent. Furthermore, AI continues to change, meaning ongoing education and training are essential to keeping skills current.
3. Using data responsibly
Ethical AI use is among the key concerns with this technology. Your organisation will need to carefully manage issues such as algorithmic bias, data privacy, and the transparency of AI models. For example, your organisation might need to ensure compliance with regulations such as the Privacy Act 1988 to protect customer data.
In addition to regulatory compliance, your business must focus on building transparent and interpretable AI systems. You also must ensure that AI systems are designed and implemented to minimise biases.
4. Legacy systems
Integrating AI technologies with existing legacy systems can be complex and costly. Many organisations struggle with outdated infrastructure that cannot support AI’s requirements for data analytics. Upgrading these systems requires significant investment and can temporarily disrupt ongoing operations, posing a major challenge for businesses looking to adopt AI.
Moreover, the integration process often involves significant changes to existing workflows and processes, which can meet resistance from employees accustomed to traditional methods. Change management strategies smoothen the transition and can help your business fully realise the benefits of AI integration.
Conclusion
AI-driven data analysis presents significant opportunities, but it does come with some challenges. Addressing issues related to data quality, talent acquisition, ethical use, and legacy systems is essential for successfully implementing AI. As the AI landscape continues to evolve, organisations that effectively navigate these challenges can realise AI’s full potential in data analysis.
MakeSense can guide your AI and Data strategy
We built our AI and data analytics services on advanced AI technologies. We specialise in delivering predictive insights, enhancing decision-making processes, and driving operational efficiency tailored to your industry’s unique requirements. Our experienced SenseMakers provide end-to-end support, backed by world-class technology platforms and powerful partnerships, ensuring your business gains the competitive edge needed in today’s marketplace.
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